arXiv:2603.12296v1 Announce Type: cross Abstract: Deep learning has achieved transformative performance across diverse domains, largely driven by the large-scale, high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentally constrained by the limited, heterogeneous, and privacy-sensitive neural recordings. Generating synthetic yet physiologically plausible brain signals has therefore emerged as a compelling way to mitigate data scarcity and enhance model capacity. This survey provides a comprehensive review of brain signal generation for BCIs, covering methodological taxonomies, benchmark experiments, evaluation metrics, and key applications. We systematically categorize existing generative algorithms into four types: knowledge-based, feature-based, model-based, and translation-based approaches. Furthermore, we benchmark existing brain signal generation approaches across four representative BCI paradigms to provide an objective performance comparison. Finally, we discuss the potentials and challenges of current generation approaches and prospect future research on accurate, data-efficient, and privacy-aware BCI systems. The benchmark codebase is publicized at https://github.com/wzwvv/DG4BCI.

arXiv:2507.20796v2 Announce Type: replace-cross Abstract: As large language models (LLMs) increasingly act as autonomous agents in markets and organizations, their behavior in strategic environments becomes economically consequential. We document that off-the-shelf LLM agents exhibit systematic deviations from payoff-sensitive behavior in canonical economic games, including excessive cooperation and limited responsiveness to incentives. We introduce a supervised fine-tuning approach that aligns agent behavior with explicit economic preferences. Specifically, we generate optimal strategies under two stylized utility specifications, homo economicus, which maximizes self-interest, and homo moralis, which incorporates Kantian universalizability, and use these utility-implied reasoning and strategies to guide fine-tuning. Fine-tuning on a small, theory-driven synthetic dataset induces persistent and interpretable shifts in strategic behavior. In applications to moral dilemmas and repeated duopoly pricing, agents aligned to different preference structures produce systematically distinct equilibrium outcomes and pricing dynamics. These results frame AI alignment in multi-agent settings as an objective-design problem and illustrate how economic theory can guide the design of strategically coherent AI agents.